Motion detection is an important component of automated video analytics. In video based on-street vehicle parking applications, cameras are often installed to monitor vehicles. In some cases it is important to identify the available parking capacity of a scene of interest. In such cases, video based parking applications have been developed to determine parking occupancy.
However, existing video-based methods for determining parking occupancy generally rely on vehicle detection. This presents a challenging problem because detecting vehicles is difficult. For example, algorithms used for vehicle detection are notoriously prone to poor performance because vehicles come in various colors, shapes, sizes, types, makes, and models. Additionally, varying weather and illumination conditions can affect the robustness of known vehicle detection means. As a result, vehicle detection often requires complicated algorithms and costly computer cycles, both in terms of time and money.
These and other problems associated with vehicle detection have created a need for improved methods and systems for video-based, on-street parking occupancy determinations.